Motion Estimation Using Point Cluster Method and Kalman Filter

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
M. Senesh ◽  
A. Wolf
Keyword(s):  
Kalman Filter ◽  
Rigid Body ◽  
Rigid Body Motion ◽  
Body Motion ◽  
Cluster Method ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The Real ◽  
Low Pass ◽  
Instantaneous Frequencies

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures—PCT, Kalman filter followed by PCT, and low pass filter followed by PCT—enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal instantaneous frequencies.

Comparison of Estimated Torques Using Low Pass Filter and Extended Kalman Filter for Induction Motor Drives

2015 ◽  
Vol 6 (1) ◽  
pp. 92
Author(s):  
Ibrahim Mohd Alsofyani ◽  
Nik Rumzi Nik Idris ◽  
Yahya A. Alamri ◽  
Tole Sutikno ◽  
Aree Wangsupphaphol ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Nonlinear Models ◽  
Motor Drives ◽  
Computational Time ◽  
Induction Motor Drives ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Calculation Process ◽  
Low Pass

<span lang="EN-US">Torque calculation process is one of the major concerns for controlling induction motors in industry, which requires very accurate state estimation of unmeasurable variables of nonlinear models. This can be solved if the variables used for torque calculation is accurately estimated.  This paper presents a torque calculation based on a voltage model represented with a low-pass filter (LPF), and an extended Kalman filter (EKF). The experimental results showed that the estimated torque at low speed based on EKF is more accurate in the expense of more complicated and larger computational time. </span>

scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
High Speed ◽  
Sensor Signal ◽  
Pass Filter ◽  
Diverse Range ◽  
Low Pass Filter ◽  
Low Pass ◽  
Using Data ◽  
Dynamic Weighing

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>

Application of Kalman Filtering and Smoothing for Airborne Gravimetry

2014 ◽  
Vol 548-549 ◽  
pp. 1192-1195
Author(s):  
Wei Zheng ◽  
Gui Bin Zhang ◽  
Rui Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Kalman Filtering ◽  
Accurate Result ◽  
System Model ◽  
Noise Filtering ◽  
Airborne Gravimetry ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Due to the interference of noise, filtering technology is applied to achieve gravity anomaly for airborne gravimetry. Kalman filtering and smoothing are discussed and implemented for data processing of airborne gravimetry in this paper. Firstly, the algorithms of Kalman filtering and smoothing are introduced. Then, the system model for solving the gravity anomaly is established which is based on the dynamic equation and the hardware design equations. Finally, the result of Kalman filtering and smoothing would be compared with digital FIR low pass filter, and it is proved that Kalman filter and smoother could obtain more accurate result than FIR low pass filter as that the solving error of Kalman filter and smoother is improved within 1 mGal compared with the theory standard obtained by GT-1A software.

scholarly journals State estimation for dynamic weighing using Kalman filter

2021 ◽  
Author(s):  
◽  
Sunethra Pitawala
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
High Speed ◽  
Sensor Signal ◽  
Pass Filter ◽  
Diverse Range ◽  
Low Pass Filter ◽  
Low Pass ◽  
Using Data ◽  
Dynamic Weighing

<p>Dynamic weighing has become an essential requirement in a diverse range of industries. Dynamic weighing is different from static weighing in that static weighing involves determining the weight while the product being weighed is stationary whereas dynamic weighing weighs the products while they are moving. Force sensors are commonly used in these weighing systems. In static weighing, the weighed object is placed stationary on the platform and the steady state of the sensor signal is used to assess the weight. However, in dynamic weighing the sensor signal may not reach the steady state during the brief time of weighing, hence the weight is assessed for example, by averaging the tail end of the signal after it has been through a low-pass filter. The resulting mass estimates can be inaccurate for faster heavier items. It is useful to consider better ways of estimating the true weight, in high speed weighing applications.  The proposed method is to employ the 1-D Kalman filter algorithm to estimate the optimal state of the signal. The improved steady state signal is then used in weight estimation. The proposed method has been tested using data collected from a loadcell when different masses pass over the loadcell. The results show a significant improvement in the filtered signal quality which is then used to improve the weight assessment.</p>

The Design of an Augmented Reality Based Rigid Body Motion Experiment System

2015 ◽  
Vol 719-720 ◽  
pp. 275-278
Author(s):  
Yang Xiang Zhang ◽  
Yun Zhu ◽  
Hui Ye
Keyword(s):  
Augmented Reality ◽  
Rigid Body ◽  
Real Time ◽  
Rigid Bodies ◽  
Rigid Body Motion ◽  
Body Motion ◽  
Motion Information ◽  
Experiment System ◽  
The Real ◽  
Physical Motion

The authors have designed an augmented reality based rigid body motion experiment system (ARRBMES), which could capture the physical motion information from the interaction between the users and virtual rigid bodies in real-time. The launcher and the container of the rigid bodies in this system are all positioned by tag cards, and the initial physical quantities of the rigid bodies are captured and analyzed through the motion information of the launcher tag card. Then ARRBMES will realize the real-time display of rigid body motion and collision events in a virtual-real fusion environment. ARRBMES can simulate the motion of rigid bodies in ideal state which cannot be achieved in the real world. As a result, the users can obtain realistic experience and the system can increase their physical intuition and cognitive experience. Moreover, ARRBMES can obtain physical information from the interaction in real-time between users and the system, which makes it a special Cyber-Physical System.

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